Optimized observable readout from single-shot images of ultracold atoms via machine learning

Single-shot images are the standard readout of experiments with ultracold atoms, the imperfect reflection of their many-body physics. The efficient extraction of observables from single-shot images is thus crucial. Here we demonstrate how artificial neural networks can optimize this extraction. In contrast to standard averaging approaches, machine learning allows both one- and two-particle densities to be accurately obtained from a drastically reduced number of single-shot images. Quantum fluctuations and correlations are directly harnessed to obtain physical observables for bosons in a tilted double-well potential at an extreme accuracy. Strikingly, machine learning also enables a reliable extraction of momentum-space observables from real-space single-shot images and vice versa. With this technique, the reconfiguration of the experimental setup between in situ and time-of-flight imaging is required only once to obtain training data, thus potentially granting an outstanding reduction in resources.

Automated summary

This study demonstrates how artificial neural networks can optimize the extraction of observables from single-shot images, accurately obtaining both one- and two-particle densities, as well as extracting momentum-space observables from real-space single-shot images. With this technique, reconfiguring the experimental setup only once to obtain training data may lead to a significant reduction in resources.